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T5Laa2-Large-WeightedLoss
hrezaei/T5Laa2-Large-WeightedLoss
T5Laa2-Large-WeightedLoss is a text generation model from hrezaei. Use it when you need the model to write or continue text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Generation
- Library
- transformers
- Model type
- t5la
- Access
- Public
- Created
- Oct 5, 2025
- Updated
- Oct 11, 2025
- SHA
- d3fe64ea
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Datasets
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
T5Laa2-Large-WeightedLoss
This model is a fine-tuned version of on the HuggingFaceFW/fineweb sample-350BT dataset. It achieves the following results on the evaluation set:
- Perplexity: 184.5759
- Loss: 5.2181
- Accuracy: 0.0373
- Lookahead Perplexity: 2089.7438
- Lookahead Loss: 7.6448
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 524288
Training results
| Training Loss | Epoch | Step | Accuracy | Lookahead Loss | Lookahead Perplexity | Validation Loss | Perplexity |
|---|---|---|---|---|---|---|---|
| 6.7799 | 0.0095 | 5000 | 0.0277 | 48.3492 | 994884838669032751104.0000 | 6.6288 | 756.5394 |
| 6.3701 | 0.0191 | 10000 | 0.0298 | 29.9422 | 10086010098930.062 | 6.2896 | 538.9212 |
| 6.1926 | 0.0286 | 15000 | 0.0310 | 17.4292 | 37103420.1102 | 6.0969 | 444.4629 |
| 6.058 | 0.0381 | 20000 | 0.0312 | 11.2096 | 73837.0007 | 5.9705 | 391.7203 |
| 5.9483 | 0.0477 | 25000 | 0.0318 | 8.7609 | 6379.6106 | 5.8987 | 364.5553 |
| 5.8936 | 0.0572 | 30000 | 0.0317 | 8.6431 | 5671.1388 | 5.9102 | 368.7617 |
| 5.9237 | 0.0668 | 35000 | 0.0342 | 8.5313 | 5070.9207 | 5.8056 | 332.1394 |
| 5.8761 | 0.0763 | 40000 | 0.0346 | 8.3857 | 4383.7940 | 5.7683 | 319.9989 |
| 5.8407 | 0.0858 | 45000 | 0.0353 | 8.6143 | 5509.9856 | 5.7586 | 316.8908 |
| 5.9167 | 0.0954 | 50000 | 0.0354 | 8.6580 | 5755.8949 | 5.7596 | 317.2192 |
| 6.0003 | 0.1049 | 55000 | 0.0359 | 8.7358 | 6221.9875 | 5.7997 | 330.1962 |
| 5.9179 | 0.1144 | 60000 | 0.0379 | 8.6974 | 5987.3140 | 5.7904 | 327.1379 |
| 5.9174 | 0.1240 | 65000 | 0.0384 | 8.5412 | 5121.6778 | 5.8089 | 333.2610 |
| 5.9954 | 0.1335 | 70000 | 0.0393 | 8.5149 | 4988.5368 | 5.8409 | 344.0949 |
| 6.0362 | 0.1431 | 75000 | 0.0400 | 8.3793 | 4356.0051 | 5.8705 | 354.4363 |
| 5.8366 | 0.1526 | 80000 | 0.0393 | 8.2272 | 3741.2679 | 5.8130 | 334.6056 |
| 6.0205 | 0.1621 | 85000 | 0.0392 | 8.5779 | 5313.1989 | 5.7982 | 329.6925 |
| 5.973 | 0.1717 | 90000 | 0.0398 | 8.8638 | 7071.0320 | 5.8446 | 345.3771 |
| 5.9258 | 0.1812 | 95000 | 0.0400 | 8.1622 | 3505.8012 | 5.7733 | 321.5895 |
| 5.8746 | 0.1907 | 100000 | 0.0403 | 8.2517 | 3834.1854 | 5.7572 | 316.4552 |
| 5.9069 | 0.2003 | 105000 | 0.0402 | 8.2418 | 3796.3680 | 5.7586 | 316.9171 |
| 5.9402 | 0.2098 | 110000 | 0.0400 | 8.5282 | 5055.2648 | 5.7703 | 320.6236 |
| 5.8692 | 0.2193 | 115000 | 0.0405 | 8.3863 | 4386.4832 | 5.7466 | 313.1122 |
| 5.9973 | 0.2289 | 120000 | 0.0394 | 8.6531 | 5727.7346 | 5.7815 | 324.2362 |
| 5.8888 | 0.2384 | 125000 | 0.0402 | 8.1073 | 3318.6616 | 5.7300 | 307.9743 |
| 5.9601 | 0.2480 | 130000 | 0.0409 | 8.6942 | 5968.4347 | 5.7525 | 314.9845 |
| 5.8925 | 0.2575 | 135000 | 0.0405 | 8.1664 | 3520.7167 | 5.7142 | 303.1319 |
| 5.8557 | 0.2670 | 140000 | 0.0401 | 7.9957 | 2968.1186 | 5.6992 | 298.6369 |
| 5.8511 | 0.2766 | 145000 | 0.0402 | 7.9752 | 2907.9587 | 5.7048 | 300.3090 |
| 5.8921 | 0.2861 | 150000 | 0.0407 | 7.9627 | 2871.8322 | 5.6648 | 288.5226 |
| 5.8002 | 0.2956 | 155000 | 0.0400 | 7.8912 | 2673.5755 | 5.6494 | 284.1184 |
| 5.8017 | 0.3052 | 160000 | 0.0400 | 8.0297 | 3070.7274 | 5.6654 | 288.7003 |
| 5.8462 | 0.3147 | 165000 | 0.0405 | 7.9691 | 2890.2133 | 5.6639 | 288.2660 |
| 5.8635 | 0.3242 | 170000 | 0.0403 | 8.2405 | 3791.4329 | 5.6655 | 288.7322 |
| 5.7894 | 0.3338 | 175000 | 0.0399 | 8.0391 | 3099.9643 | 5.6634 | 288.1246 |
| 5.9122 | 0.3433 | 180000 | 0.0411 | 8.0436 | 3113.8276 | 5.6549 | 285.6828 |
| 5.8401 | 0.3529 | 185000 | 0.0409 | 8.2639 | 3881.1753 | 5.6554 | 285.8374 |
| 5.8252 | 0.3624 | 190000 | 0.0408 | 7.9751 | 2907.7520 | 5.6592 | 286.9267 |
| 5.8975 | 0.3719 | 195000 | 0.0405 | 7.9789 | 2918.8320 | 5.6414 | 281.8590 |
| 5.8008 | 0.3815 | 200000 | 0.0393 | 7.8772 | 2636.5364 | 5.6323 | 279.2986 |
| 5.776 | 0.3910 | 205000 | 0.0401 | 7.9352 | 2793.9517 | 5.6288 | 278.3158 |
| 5.8825 | 0.4005 | 210000 | 0.0401 | 7.9805 | 2923.3879 | 5.6192 | 275.6601 |
| 5.7651 | 0.4101 | 215000 | 0.0400 | 7.9989 | 2977.7573 | 5.5993 | 270.2366 |
| 5.7721 | 0.4196 | 220000 | 0.0406 | 7.8928 | 2677.9319 | 5.5979 | 269.8660 |
| 5.8312 | 0.4292 | 225000 | 0.0396 | 8.0192 | 3038.8659 | 5.6054 | 271.8775 |
| 5.7752 | 0.4387 | 230000 | 0.0405 | 7.8009 | 2442.8390 | 5.5823 | 265.6886 |
| 5.8101 | 0.4482 | 235000 | 0.0397 | 7.8881 | 2665.3761 | 5.5903 | 267.8042 |
| 5.7115 | 0.4578 | 240000 | 0.0400 | 7.9381 | 2802.0555 | 5.5694 | 262.2645 |
| 5.7196 | 0.4673 | 245000 | 0.0394 | 7.8143 | 2475.7568 | 5.5596 | 259.7104 |
| 5.6944 | 0.4768 | 250000 | 0.0409 | 7.8772 | 2636.5595 | 5.5478 | 256.6677 |
| 5.6823 | 0.4864 | 255000 | 0.0395 | 7.7952 | 2428.9769 | 5.5298 | 252.0854 |
| 5.674 | 0.4959 | 260000 | 0.0399 | 7.8926 | 2677.4051 | 5.5318 | 252.5954 |
| 5.6606 | 0.5054 | 265000 | 0.0400 | 7.8189 | 2487.2421 | 5.5178 | 249.0964 |
| 5.7097 | 0.5150 | 270000 | 0.0395 | 7.8465 | 2556.6945 | 5.5101 | 247.1704 |
| 5.7047 | 0.5245 | 275000 | 0.0402 | 7.7667 | 2360.7532 | 5.5015 | 245.0604 |
| 5.6797 | 0.5341 | 280000 | 0.0397 | 7.8969 | 2688.8862 | 5.4982 | 244.2633 |
| 5.6739 | 0.5436 | 285000 | 0.0398 | 8.0241 | 3053.8151 | 5.4930 | 242.9751 |
| 5.6826 | 0.5531 | 290000 | 0.0397 | 7.9106 | 2726.0737 | 5.4990 | 244.4371 |
| 5.7864 | 0.5627 | 295000 | 0.0397 | 7.8498 | 2565.2361 | 5.4800 | 239.8371 |
| 5.6506 | 0.5722 | 300000 | 0.0401 | 7.9694 | 2891.0478 | 5.4805 | 239.9755 |
| 5.6403 | 0.5817 | 305000 | 0.0390 | 7.8301 | 2515.0960 | 5.4738 | 238.3728 |
| 5.6538 | 0.5913 | 310000 | 0.0398 | 7.8934 | 2679.4140 | 5.4811 | 240.0990 |
| 5.6665 | 0.6008 | 315000 | 0.0399 | 7.8407 | 2541.9513 | 5.4566 | 234.3107 |
| 5.5755 | 0.6104 | 320000 | 232.2292 | 5.4477 | 0.0395 | 2593.5901 | 7.8608 |
| 5.641 | 0.6199 | 325000 | 231.2019 | 5.4433 | 0.0394 | 2925.4813 | 7.9812 |
| 5.6113 | 0.6294 | 330000 | 229.7290 | 5.4369 | 0.0391 | 2471.8038 | 7.8127 |
| 5.6697 | 0.6390 | 335000 | 229.2882 | 5.4350 | 0.0394 | 2709.0417 | 7.9044 |
| 5.6425 | 0.6485 | 340000 | 228.1699 | 5.4301 | 0.0397 | 2550.9241 | 7.8442 |
| 5.626 | 0.6580 | 345000 | 226.4364 | 5.4225 | 0.0391 | 2601.7519 | 7.8639 |
| 5.5888 | 0.6676 | 350000 | 225.6680 | 5.4191 | 0.0394 | 2929.0955 | 7.9824 |
| 5.5793 | 0.6771 | 355000 | 224.6552 | 5.4146 | 0.0389 | 3111.5742 | 8.0429 |
| 5.5751 | 0.6866 | 360000 | 222.2638 | 5.4039 | 0.0385 | 2507.0953 | 7.8269 |
| 5.5659 | 0.6962 | 365000 | 219.8554 | 5.3930 | 0.0388 | 2442.3736 | 7.8007 |
| 5.6128 | 0.7057 | 370000 | 217.8869 | 5.3840 | 0.0385 | 2365.9076 | 7.7689 |
| 5.5471 | 0.7153 | 375000 | 216.1903 | 5.3762 | 0.0380 | 2286.1222 | 7.7346 |
| 5.5468 | 0.7248 | 380000 | 214.2540 | 5.3672 | 0.0387 | 2292.4794 | 7.7374 |
| 5.5354 | 0.7343 | 385000 | 211.9470 | 5.3563 | 0.0383 | 2245.4357 | 7.7167 |
| 5.5659 | 0.7439 | 390000 | 210.5267 | 5.3496 | 0.0384 | 2303.3757 | 7.7421 |
| 5.5114 | 0.7534 | 395000 | 209.1623 | 5.3431 | 0.0382 | 2344.8778 | 7.7600 |
| 5.5024 | 0.7629 | 400000 | 207.8823 | 5.3370 | 0.0383 | 2387.2802 | 7.7779 |
| 5.5723 | 0.7725 | 405000 | 206.5772 | 5.3307 | 0.0381 | 2312.2991 | 7.7460 |
| 5.4679 | 0.7820 | 410000 | 204.4713 | 5.3204 | 0.0384 | 2225.5773 | 7.7078 |
| 5.5022 | 0.7915 | 415000 | 202.6541 | 5.3115 | 0.0379 | 2216.8935 | 7.7039 |
| 5.4582 | 0.8011 | 420000 | 202.3064 | 5.3098 | 0.0382 | 2253.1685 | 7.7201 |
| 5.4716 | 0.8106 | 425000 | 199.9121 | 5.2979 | 0.0379 | 2201.0493 | 7.6967 |
| 5.4742 | 0.8202 | 430000 | 199.2166 | 5.2944 | 0.0379 | 2221.2136 | 7.7058 |
| 5.456 | 0.8297 | 435000 | 197.5286 | 5.2859 | 0.0378 | 2275.2305 | 7.7298 |
| 5.4751 | 0.8392 | 440000 | 196.2503 | 5.2794 | 0.0380 | 2230.8815 | 7.7102 |
| 5.4628 | 0.8488 | 445000 | 195.3121 | 5.2746 | 0.0379 | 2281.6319 | 7.7326 |
| 5.3535 | 0.8583 | 450000 | 195.0385 | 5.2732 | 0.0377 | 2178.3606 | 7.6863 |
| 5.5193 | 0.8678 | 455000 | 193.3610 | 5.2646 | 0.0380 | 2221.9979 | 7.7062 |
| 5.4747 | 0.8774 | 460000 | 192.5007 | 5.2601 | 0.0374 | 2183.6563 | 7.6888 |
| 5.4077 | 0.8869 | 465000 | 191.1322 | 5.2530 | 0.0375 | 2137.0722 | 7.6672 |
| 5.4288 | 0.8965 | 470000 | 190.2730 | 5.2485 | 0.0377 | 2108.7450 | 7.6538 |
| 5.4653 | 0.9060 | 475000 | 189.7327 | 5.2456 | 0.0377 | 2132.3223 | 7.6650 |
| 5.3929 | 0.9155 | 480000 | 188.8477 | 5.2409 | 0.0376 | 2122.3287 | 7.6603 |
| 5.405 | 0.9251 | 485000 | 187.7804 | 5.2353 | 0.0374 | 2107.0023 | 7.6530 |
| 5.4504 | 0.9346 | 490000 | 187.0694 | 5.2315 | 0.0374 | 2111.6347 | 7.6552 |
| 5.4217 | 0.9441 | 495000 | 186.9062 | 5.2306 | 0.0374 | 2110.7966 | 7.6548 |
| 5.4109 | 0.9537 | 500000 | 185.9346 | 5.2254 | 0.0372 | 2099.9909 | 7.6497 |
| 5.3892 | 1.0095 | 505000 | 185.5628 | 5.2234 | 0.0374 | 2097.9349 | 7.6487 |
| 5.3806 | 1.0191 | 510000 | 184.9853 | 5.2203 | 0.0374 | 2092.8939 | 7.6463 |
| 5.4174 | 1.0286 | 515000 | 184.8205 | 5.2194 | 0.0375 | 2090.1261 | 7.6450 |
| 5.4017 | 1.0381 | 520000 | 184.6505 | 5.2185 | 0.0374 | 2090.0473 | 7.6449 |
Framework versions
- Transformers 4.57.0.dev0
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.1
- Task
- Text Generation
- Library
- transformers
- Type
- t5la
- Created
- Oct 5, 2025
- Updated
- Oct 11, 2025